Overview

Dataset statistics

Number of variables26
Number of observations102825
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.4 MiB
Average record size in memory208.0 B

Variable types

Numeric18
Categorical8

Alerts

df_index is uniformly distributed Uniform
Unnamed: 0 is uniformly distributed Uniform
id is uniformly distributed Uniform
df_index has unique values Unique
Unnamed: 0 has unique values Unique
id has unique values Unique
Inflight wifi service has 3075 (3.0%) zeros Zeros
Departure/Arrival time convenient has 5259 (5.1%) zeros Zeros
Ease of Online booking has 4443 (4.3%) zeros Zeros
Online boarding has 2428 (2.4%) zeros Zeros
Departure Delay in Minutes has 58649 (57.0%) zeros Zeros
Arrival Delay in Minutes has 58135 (56.5%) zeros Zeros

Reproduction

Analysis started2023-02-18 17:42:07.661381
Analysis finished2023-02-18 17:43:27.906487
Duration1 minute and 20.25 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct102825
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51941.91747
Minimum0
Maximum103903
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:43:28.223841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5193.2
Q125968
median51939
Q377911
95-th percentile98703.8
Maximum103903
Range103903
Interquartile range (IQR)51943

Descriptive statistics

Standard deviation29993.15899
Coefficient of variation (CV)0.5774364993
Kurtosis-1.200183173
Mean51941.91747
Median Absolute Deviation (MAD)25972
Skewness0.0001207897413
Sum5340927664
Variance899589586.2
MonotonicityStrictly increasing
2023-02-18T12:43:28.418021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
692631
 
< 0.1%
692741
 
< 0.1%
692731
 
< 0.1%
692721
 
< 0.1%
692711
 
< 0.1%
692701
 
< 0.1%
692691
 
< 0.1%
692681
 
< 0.1%
692671
 
< 0.1%
Other values (102815)102815
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
1039031
< 0.1%
1039021
< 0.1%
1039011
< 0.1%
1039001
< 0.1%
1038991
< 0.1%
1038981
< 0.1%
1038971
< 0.1%
1038961
< 0.1%
1038951
< 0.1%
1038941
< 0.1%

Unnamed: 0
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct102825
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51941.91747
Minimum0
Maximum103903
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:43:28.612123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5193.2
Q125968
median51939
Q377911
95-th percentile98703.8
Maximum103903
Range103903
Interquartile range (IQR)51943

Descriptive statistics

Standard deviation29993.15899
Coefficient of variation (CV)0.5774364993
Kurtosis-1.200183173
Mean51941.91747
Median Absolute Deviation (MAD)25972
Skewness0.0001207897413
Sum5340927664
Variance899589586.2
MonotonicityStrictly increasing
2023-02-18T12:43:28.815461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
692631
 
< 0.1%
692741
 
< 0.1%
692731
 
< 0.1%
692721
 
< 0.1%
692711
 
< 0.1%
692701
 
< 0.1%
692691
 
< 0.1%
692681
 
< 0.1%
692671
 
< 0.1%
Other values (102815)102815
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
1039031
< 0.1%
1039021
< 0.1%
1039011
< 0.1%
1039001
< 0.1%
1038991
< 0.1%
1038981
< 0.1%
1038971
< 0.1%
1038961
< 0.1%
1038951
< 0.1%
1038941
< 0.1%

id
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct102825
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64979.96573
Minimum1
Maximum129880
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:43:29.035137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6601.2
Q132590
median64939
Q397431
95-th percentile123447.4
Maximum129880
Range129879
Interquartile range (IQR)64841

Descriptive statistics

Standard deviation37471.2646
Coefficient of variation (CV)0.5766587313
Kurtosis-1.198426389
Mean64979.96573
Median Absolute Deviation (MAD)32424
Skewness0.001639047985
Sum6681564976
Variance1404095670
MonotonicityNot monotonic
2023-02-18T12:43:29.302508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
701721
 
< 0.1%
609211
 
< 0.1%
1185741
 
< 0.1%
235291
 
< 0.1%
162721
 
< 0.1%
584381
 
< 0.1%
23521
 
< 0.1%
659081
 
< 0.1%
670571
 
< 0.1%
184811
 
< 0.1%
Other values (102815)102815
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
1298801
< 0.1%
1298791
< 0.1%
1298781
< 0.1%
1298751
< 0.1%
1298741
< 0.1%
1298731
< 0.1%
1298711
< 0.1%
1298701
< 0.1%
1298691
< 0.1%
1298671
< 0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
Female
52169 
Male
50656 

Length

Max length6
Median length6
Mean length5.01471432
Min length4

Characters and Unicode

Total characters515638
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Female52169
50.7%
Male50656
49.3%

Length

2023-02-18T12:43:29.653219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:43:30.019992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
female52169
50.7%
male50656
49.3%

Most occurring characters

ValueCountFrequency (%)
e154994
30.1%
a102825
19.9%
l102825
19.9%
F52169
 
10.1%
m52169
 
10.1%
M50656
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter412813
80.1%
Uppercase Letter102825
 
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e154994
37.5%
a102825
24.9%
l102825
24.9%
m52169
 
12.6%
Uppercase Letter
ValueCountFrequency (%)
F52169
50.7%
M50656
49.3%

Most occurring scripts

ValueCountFrequency (%)
Latin515638
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e154994
30.1%
a102825
19.9%
l102825
19.9%
F52169
 
10.1%
m52169
 
10.1%
M50656
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII515638
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e154994
30.1%
a102825
19.9%
l102825
19.9%
F52169
 
10.1%
m52169
 
10.1%
M50656
 
9.8%

Customer Type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
Loyal Customer
84003 
disloyal Customer
18822 

Length

Max length17
Median length14
Mean length14.54914661
Min length14

Characters and Unicode

Total characters1496016
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLoyal Customer
2nd rowdisloyal Customer
3rd rowLoyal Customer
4th rowLoyal Customer
5th rowLoyal Customer

Common Values

ValueCountFrequency (%)
Loyal Customer84003
81.7%
disloyal Customer18822
 
18.3%

Length

2023-02-18T12:43:30.237232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:43:30.515773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
customer102825
50.0%
loyal84003
40.8%
disloyal18822
 
9.2%

Most occurring characters

ValueCountFrequency (%)
o205650
13.7%
l121647
 
8.1%
s121647
 
8.1%
y102825
 
6.9%
a102825
 
6.9%
102825
 
6.9%
C102825
 
6.9%
u102825
 
6.9%
t102825
 
6.9%
m102825
 
6.9%
Other values (5)327297
21.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1206363
80.6%
Uppercase Letter186828
 
12.5%
Space Separator102825
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o205650
17.0%
l121647
10.1%
s121647
10.1%
y102825
8.5%
a102825
8.5%
u102825
8.5%
t102825
8.5%
m102825
8.5%
e102825
8.5%
r102825
8.5%
Other values (2)37644
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
C102825
55.0%
L84003
45.0%
Space Separator
ValueCountFrequency (%)
102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1393191
93.1%
Common102825
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o205650
14.8%
l121647
8.7%
s121647
8.7%
y102825
7.4%
a102825
7.4%
C102825
7.4%
u102825
7.4%
t102825
7.4%
m102825
7.4%
e102825
7.4%
Other values (4)224472
16.1%
Common
ValueCountFrequency (%)
102825
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1496016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o205650
13.7%
l121647
 
8.1%
s121647
 
8.1%
y102825
 
6.9%
a102825
 
6.9%
102825
 
6.9%
C102825
 
6.9%
u102825
 
6.9%
t102825
 
6.9%
m102825
 
6.9%
Other values (5)327297
21.9%

Age
Real number (ℝ≥0)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.37699976
Minimum7
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:43:30.819286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14
Q127
median40
Q351
95-th percentile64
Maximum85
Range78
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.10312865
Coefficient of variation (CV)0.3835520417
Kurtosis-0.7198928334
Mean39.37699976
Median Absolute Deviation (MAD)12
Skewness-0.004753400847
Sum4048940
Variance228.1044951
MonotonicityNot monotonic
2023-02-18T12:43:31.117220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
392938
 
2.9%
252769
 
2.7%
402544
 
2.5%
442462
 
2.4%
422431
 
2.4%
412427
 
2.4%
222338
 
2.3%
452319
 
2.3%
232318
 
2.3%
472309
 
2.2%
Other values (65)77970
75.8%
ValueCountFrequency (%)
7557
0.5%
8634
0.6%
9682
0.7%
10670
0.7%
11667
0.6%
12630
0.6%
13619
0.6%
14701
0.7%
15807
0.8%
16884
0.9%
ValueCountFrequency (%)
8517
 
< 0.1%
8075
 
0.1%
7940
 
< 0.1%
7830
 
< 0.1%
7785
0.1%
7645
 
< 0.1%
7560
 
0.1%
7444
 
< 0.1%
7349
 
< 0.1%
72198
0.2%

Type of Travel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
Business travel
70897 
Personal Travel
31928 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters1542375
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersonal Travel
2nd rowBusiness travel
3rd rowBusiness travel
4th rowBusiness travel
5th rowBusiness travel

Common Values

ValueCountFrequency (%)
Business travel70897
68.9%
Personal Travel31928
31.1%

Length

2023-02-18T12:43:31.811730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:43:32.042638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
travel102825
50.0%
business70897
34.5%
personal31928
 
15.5%

Most occurring characters

ValueCountFrequency (%)
s244619
15.9%
e205650
13.3%
r134753
8.7%
a134753
8.7%
l134753
8.7%
n102825
6.7%
102825
6.7%
v102825
6.7%
B70897
 
4.6%
u70897
 
4.6%
Other values (5)237578
15.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1304797
84.6%
Uppercase Letter134753
 
8.7%
Space Separator102825
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s244619
18.7%
e205650
15.8%
r134753
10.3%
a134753
10.3%
l134753
10.3%
n102825
7.9%
v102825
7.9%
u70897
 
5.4%
i70897
 
5.4%
t70897
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
B70897
52.6%
P31928
23.7%
T31928
23.7%
Space Separator
ValueCountFrequency (%)
102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1439550
93.3%
Common102825
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
s244619
17.0%
e205650
14.3%
r134753
9.4%
a134753
9.4%
l134753
9.4%
n102825
7.1%
v102825
7.1%
B70897
 
4.9%
u70897
 
4.9%
i70897
 
4.9%
Other values (4)166681
11.6%
Common
ValueCountFrequency (%)
102825
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1542375
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s244619
15.9%
e205650
13.3%
r134753
8.7%
a134753
8.7%
l134753
8.7%
n102825
6.7%
102825
6.7%
v102825
6.7%
B70897
 
4.6%
u70897
 
4.6%
Other values (5)237578
15.4%

Class
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
Business
49125 
Eco
46278 
Eco Plus
7422 

Length

Max length8
Median length8
Mean length5.749671772
Min length3

Characters and Unicode

Total characters591210
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEco Plus
2nd rowBusiness
3rd rowBusiness
4th rowBusiness
5th rowBusiness

Common Values

ValueCountFrequency (%)
Business49125
47.8%
Eco46278
45.0%
Eco Plus7422
 
7.2%

Length

2023-02-18T12:43:32.280655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:43:32.474243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
eco53700
48.7%
business49125
44.6%
plus7422
 
6.7%

Most occurring characters

ValueCountFrequency (%)
s154797
26.2%
u56547
 
9.6%
E53700
 
9.1%
c53700
 
9.1%
o53700
 
9.1%
B49125
 
8.3%
i49125
 
8.3%
n49125
 
8.3%
e49125
 
8.3%
7422
 
1.3%
Other values (2)14844
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter473541
80.1%
Uppercase Letter110247
 
18.6%
Space Separator7422
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s154797
32.7%
u56547
 
11.9%
c53700
 
11.3%
o53700
 
11.3%
i49125
 
10.4%
n49125
 
10.4%
e49125
 
10.4%
l7422
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
E53700
48.7%
B49125
44.6%
P7422
 
6.7%
Space Separator
ValueCountFrequency (%)
7422
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin583788
98.7%
Common7422
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
s154797
26.5%
u56547
 
9.7%
E53700
 
9.2%
c53700
 
9.2%
o53700
 
9.2%
B49125
 
8.4%
i49125
 
8.4%
n49125
 
8.4%
e49125
 
8.4%
P7422
 
1.3%
Common
ValueCountFrequency (%)
7422
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII591210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s154797
26.2%
u56547
 
9.6%
E53700
 
9.1%
c53700
 
9.1%
o53700
 
9.1%
B49125
 
8.3%
i49125
 
8.3%
n49125
 
8.3%
e49125
 
8.3%
7422
 
1.3%
Other values (2)14844
 
2.5%

Flight Distance
Real number (ℝ≥0)

Distinct3798
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1184.68708
Minimum31
Maximum4243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:43:32.681845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile175
Q1413
median841
Q31735
95-th percentile3372
Maximum4243
Range4212
Interquartile range (IQR)1322

Descriptive statistics

Standard deviation992.2100985
Coefficient of variation (CV)0.8375292643
Kurtosis0.2532586348
Mean1184.68708
Median Absolute Deviation (MAD)514
Skewness1.107696144
Sum121815449
Variance984480.8796
MonotonicityNot monotonic
2023-02-18T12:43:32.902138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337654
 
0.6%
594395
 
0.4%
404390
 
0.4%
862368
 
0.4%
2475361
 
0.4%
447358
 
0.3%
236350
 
0.3%
399330
 
0.3%
308329
 
0.3%
192328
 
0.3%
Other values (3788)98962
96.2%
ValueCountFrequency (%)
318
 
< 0.1%
568
 
< 0.1%
67124
0.1%
7358
0.1%
7430
 
< 0.1%
761
 
< 0.1%
7741
 
< 0.1%
7830
 
< 0.1%
802
 
< 0.1%
827
 
< 0.1%
ValueCountFrequency (%)
424317
< 0.1%
400011
< 0.1%
39995
 
< 0.1%
39988
< 0.1%
39979
< 0.1%
39968
< 0.1%
39956
 
< 0.1%
39946
 
< 0.1%
399313
< 0.1%
39926
 
< 0.1%

Inflight wifi service
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.730201799
Minimum0
Maximum5
Zeros3075
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:43:33.059543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.328309363
Coefficient of variation (CV)0.4865242429
Kurtosis-0.8468882893
Mean2.730201799
Median Absolute Deviation (MAD)1
Skewness0.04005442271
Sum280733
Variance1.764405764
MonotonicityNot monotonic
2023-02-18T12:43:33.182537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
325583
24.9%
225553
24.9%
419592
19.1%
117650
17.2%
511372
11.1%
03075
 
3.0%
ValueCountFrequency (%)
03075
 
3.0%
117650
17.2%
225553
24.9%
325583
24.9%
419592
19.1%
511372
11.1%
ValueCountFrequency (%)
511372
11.1%
419592
19.1%
325583
24.9%
225553
24.9%
117650
17.2%
03075
 
3.0%

Departure/Arrival time convenient
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.05953805
Minimum0
Maximum5
Zeros5259
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:43:33.641945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.52573781
Coefficient of variation (CV)0.4986824107
Kurtosis-1.038883861
Mean3.05953805
Median Absolute Deviation (MAD)1
Skewness-0.3338621462
Sum314597
Variance2.327875866
MonotonicityNot monotonic
2023-02-18T12:43:33.874761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
425255
24.6%
522179
21.6%
317773
17.3%
217004
16.5%
115355
14.9%
05259
 
5.1%
ValueCountFrequency (%)
05259
 
5.1%
115355
14.9%
217004
16.5%
317773
17.3%
425255
24.6%
522179
21.6%
ValueCountFrequency (%)
522179
21.6%
425255
24.6%
317773
17.3%
217004
16.5%
115355
14.9%
05259
 
5.1%

Ease of Online booking
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.756946268
Minimum0
Maximum5
Zeros4443
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:43:34.567354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.399237606
Coefficient of variation (CV)0.5075316928
Kurtosis-0.9109943353
Mean2.756946268
Median Absolute Deviation (MAD)1
Skewness-0.0185530272
Sum283483
Variance1.957865879
MonotonicityNot monotonic
2023-02-18T12:43:34.800492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
324198
23.5%
223742
23.1%
419369
18.8%
117359
16.9%
513714
13.3%
04443
 
4.3%
ValueCountFrequency (%)
04443
 
4.3%
117359
16.9%
223742
23.1%
324198
23.5%
419369
18.8%
513714
13.3%
ValueCountFrequency (%)
513714
13.3%
419369
18.8%
324198
23.5%
223742
23.1%
117359
16.9%
04443
 
4.3%

Gate location
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.976075857
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:43:34.926107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.277811082
Coefficient of variation (CV)0.4293610591
Kurtosis-1.030846056
Mean2.976075857
Median Absolute Deviation (MAD)1
Skewness-0.05801843526
Sum306015
Variance1.632801161
MonotonicityNot monotonic
2023-02-18T12:43:35.055559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
328270
27.5%
424153
23.5%
219272
18.7%
117399
16.9%
513730
13.4%
01
 
< 0.1%
ValueCountFrequency (%)
01
 
< 0.1%
117399
16.9%
219272
18.7%
328270
27.5%
424153
23.5%
513730
13.4%
ValueCountFrequency (%)
513730
13.4%
424153
23.5%
328270
27.5%
219272
18.7%
117399
16.9%
01
 
< 0.1%

Food and drink
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.204026258
Minimum0
Maximum5
Zeros77
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:43:35.367716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.32901013
Coefficient of variation (CV)0.4147937697
Kurtosis-1.149542607
Mean3.204026258
Median Absolute Deviation (MAD)1
Skewness-0.1502251435
Sum329454
Variance1.766267925
MonotonicityNot monotonic
2023-02-18T12:43:36.018654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
424083
23.4%
522141
21.5%
322069
21.5%
221755
21.2%
112700
12.4%
077
 
0.1%
ValueCountFrequency (%)
077
 
0.1%
112700
12.4%
221755
21.2%
322069
21.5%
424083
23.4%
522141
21.5%
ValueCountFrequency (%)
522141
21.5%
424083
23.4%
322069
21.5%
221755
21.2%
112700
12.4%
077
 
0.1%

Online boarding
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.250162898
Minimum0
Maximum5
Zeros2428
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:43:36.166304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.349176046
Coefficient of variation (CV)0.4151102847
Kurtosis-0.6967311813
Mean3.250162898
Median Absolute Deviation (MAD)1
Skewness-0.4555493449
Sum334198
Variance1.820276003
MonotonicityNot monotonic
2023-02-18T12:43:36.311227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
430471
29.6%
321610
21.0%
520460
19.9%
217328
16.9%
110528
 
10.2%
02428
 
2.4%
ValueCountFrequency (%)
02428
 
2.4%
110528
 
10.2%
217328
16.9%
321610
21.0%
430471
29.6%
520460
19.9%
ValueCountFrequency (%)
520460
19.9%
430471
29.6%
321610
21.0%
217328
16.9%
110528
 
10.2%
02428
 
2.4%

Seat comfort
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
4
31470 
5
26214 
3
18497 
2
14721 
1
11923 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters102825
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row1
3rd row5
4th row2
5th row5

Common Values

ValueCountFrequency (%)
431470
30.6%
526214
25.5%
318497
18.0%
214721
14.3%
111923
 
11.6%

Length

2023-02-18T12:43:36.468577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:43:36.610223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
431470
30.6%
526214
25.5%
318497
18.0%
214721
14.3%
111923
 
11.6%

Most occurring characters

ValueCountFrequency (%)
431470
30.6%
526214
25.5%
318497
18.0%
214721
14.3%
111923
 
11.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number102825
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
431470
30.6%
526214
25.5%
318497
18.0%
214721
14.3%
111923
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
Common102825
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
431470
30.6%
526214
25.5%
318497
18.0%
214721
14.3%
111923
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII102825
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
431470
30.6%
526214
25.5%
318497
18.0%
214721
14.3%
111923
 
11.6%

Inflight entertainment
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.359776319
Minimum0
Maximum5
Zeros14
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:43:36.762493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.333567497
Coefficient of variation (CV)0.3969215121
Kurtosis-1.061728834
Mean3.359776319
Median Absolute Deviation (MAD)1
Skewness-0.3665182728
Sum345469
Variance1.778402268
MonotonicityNot monotonic
2023-02-18T12:43:36.874666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
429144
28.3%
525021
24.3%
318830
18.3%
217482
17.0%
112334
12.0%
014
 
< 0.1%
ValueCountFrequency (%)
014
 
< 0.1%
112334
12.0%
217482
17.0%
318830
18.3%
429144
28.3%
525021
24.3%
ValueCountFrequency (%)
525021
24.3%
429144
28.3%
318830
18.3%
217482
17.0%
112334
12.0%
014
 
< 0.1%

On-board service
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.384741065
Minimum0
Maximum5
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:43:37.014753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.287822549
Coefficient of variation (CV)0.3804788975
Kurtosis-0.8876611376
Mean3.384741065
Median Absolute Deviation (MAD)1
Skewness-0.4237735165
Sum348036
Variance1.658486917
MonotonicityNot monotonic
2023-02-18T12:43:37.128439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
430637
29.8%
523425
22.8%
322579
22.0%
214445
14.0%
111736
 
11.4%
03
 
< 0.1%
ValueCountFrequency (%)
03
 
< 0.1%
111736
 
11.4%
214445
14.0%
322579
22.0%
430637
29.8%
523425
22.8%
ValueCountFrequency (%)
523425
22.8%
430637
29.8%
322579
22.0%
214445
14.0%
111736
 
11.4%
03
 
< 0.1%

Leg room service
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.347600292
Minimum0
Maximum5
Zeros472
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:43:37.244680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.317298939
Coefficient of variation (CV)0.393505444
Kurtosis-0.9861641713
Mean3.347600292
Median Absolute Deviation (MAD)1
Skewness-0.3461964615
Sum344217
Variance1.735276495
MonotonicityNot monotonic
2023-02-18T12:43:37.358098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
428381
27.6%
524400
23.7%
319849
19.3%
219423
18.9%
110300
 
10.0%
0472
 
0.5%
ValueCountFrequency (%)
0472
 
0.5%
110300
 
10.0%
219423
18.9%
319849
19.3%
428381
27.6%
524400
23.7%
ValueCountFrequency (%)
524400
23.7%
428381
27.6%
319849
19.3%
219423
18.9%
110300
 
10.0%
0472
 
0.5%

Baggage handling
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
4
36962 
5
26849 
3
20401 
2
11420 
1
7193 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters102825
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row4
4th row3
5th row4

Common Values

ValueCountFrequency (%)
436962
35.9%
526849
26.1%
320401
19.8%
211420
 
11.1%
17193
 
7.0%

Length

2023-02-18T12:43:37.490288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:43:37.626372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
436962
35.9%
526849
26.1%
320401
19.8%
211420
 
11.1%
17193
 
7.0%

Most occurring characters

ValueCountFrequency (%)
436962
35.9%
526849
26.1%
320401
19.8%
211420
 
11.1%
17193
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number102825
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
436962
35.9%
526849
26.1%
320401
19.8%
211420
 
11.1%
17193
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
Common102825
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
436962
35.9%
526849
26.1%
320401
19.8%
211420
 
11.1%
17193
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII102825
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
436962
35.9%
526849
26.1%
320401
19.8%
211420
 
11.1%
17193
 
7.0%

Checkin service
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
4
28761 
3
28248 
5
20362 
1
12737 
2
12717 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters102825
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row4
4th row1
5th row3

Common Values

ValueCountFrequency (%)
428761
28.0%
328248
27.5%
520362
19.8%
112737
12.4%
212717
12.4%

Length

2023-02-18T12:43:37.765372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:43:37.940391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
428761
28.0%
328248
27.5%
520362
19.8%
112737
12.4%
212717
12.4%

Most occurring characters

ValueCountFrequency (%)
428761
28.0%
328248
27.5%
520362
19.8%
112737
12.4%
212717
12.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number102825
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
428761
28.0%
328248
27.5%
520362
19.8%
112737
12.4%
212717
12.4%

Most occurring scripts

ValueCountFrequency (%)
Common102825
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
428761
28.0%
328248
27.5%
520362
19.8%
112737
12.4%
212717
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII102825
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
428761
28.0%
328248
27.5%
520362
19.8%
112737
12.4%
212717
12.4%

Inflight service
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.645057136
Minimum0
Maximum5
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:43:38.090478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.173754765
Coefficient of variation (CV)0.3220127205
Kurtosis-0.3474748905
Mean3.645057136
Median Absolute Deviation (MAD)1
Skewness-0.6950352011
Sum374803
Variance1.377700248
MonotonicityNot monotonic
2023-02-18T12:43:38.232286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
437676
36.6%
526922
26.2%
319977
19.4%
211311
 
11.0%
16936
 
6.7%
03
 
< 0.1%
ValueCountFrequency (%)
03
 
< 0.1%
16936
 
6.7%
211311
 
11.0%
319977
19.4%
437676
36.6%
526922
26.2%
ValueCountFrequency (%)
526922
26.2%
437676
36.6%
319977
19.4%
211311
 
11.0%
16936
 
6.7%
03
 
< 0.1%

Cleanliness
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.286204717
Minimum0
Maximum5
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:43:38.381526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.311676792
Coefficient of variation (CV)0.3991464029
Kurtosis-1.011340887
Mean3.286204717
Median Absolute Deviation (MAD)1
Skewness-0.2997111877
Sum337904
Variance1.720496007
MonotonicityNot monotonic
2023-02-18T12:43:38.513850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
426885
26.1%
324376
23.7%
522432
21.8%
215955
15.5%
113166
12.8%
011
 
< 0.1%
ValueCountFrequency (%)
011
 
< 0.1%
113166
12.8%
215955
15.5%
324376
23.7%
426885
26.1%
522432
21.8%
ValueCountFrequency (%)
522432
21.8%
426885
26.1%
324376
23.7%
215955
15.5%
113166
12.8%
011
 
< 0.1%

Departure Delay in Minutes
Real number (ℝ≥0)

ZEROS

Distinct214
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.32035983
Minimum0
Maximum215
Zeros58649
Zeros (%)57.0%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:43:38.731309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile69
Maximum215
Range215
Interquartile range (IQR)12

Descriptive statistics

Standard deviation26.68809684
Coefficient of variation (CV)2.166178358
Kurtosis12.45225849
Mean12.32035983
Median Absolute Deviation (MAD)0
Skewness3.280161934
Sum1266841
Variance712.2545128
MonotonicityNot monotonic
2023-02-18T12:43:38.895434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
058649
57.0%
12947
 
2.9%
22272
 
2.2%
32007
 
2.0%
41852
 
1.8%
51692
 
1.6%
61515
 
1.5%
71392
 
1.4%
81295
 
1.3%
91254
 
1.2%
Other values (204)27950
27.2%
ValueCountFrequency (%)
058649
57.0%
12947
 
2.9%
22272
 
2.2%
32007
 
2.0%
41852
 
1.8%
51692
 
1.6%
61515
 
1.5%
71392
 
1.4%
81295
 
1.3%
91254
 
1.2%
ValueCountFrequency (%)
2151
 
< 0.1%
2142
 
< 0.1%
2125
< 0.1%
2112
 
< 0.1%
2102
 
< 0.1%
2094
< 0.1%
2071
 
< 0.1%
2062
 
< 0.1%
2053
< 0.1%
2043
< 0.1%

Arrival Delay in Minutes
Real number (ℝ≥0)

ZEROS

Distinct320
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.7212316
Minimum0
Maximum242
Zeros58135
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:43:39.057904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile70
Maximum242
Range242
Interquartile range (IQR)12

Descriptive statistics

Standard deviation27.25974126
Coefficient of variation (CV)2.14285394
Kurtosis12.76562575
Mean12.7212316
Median Absolute Deviation (MAD)0
Skewness3.300817762
Sum1308060.639
Variance743.0934935
MonotonicityNot monotonic
2023-02-18T12:43:39.233425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
058135
56.5%
12211
 
2.2%
22061
 
2.0%
31952
 
1.9%
41906
 
1.9%
51657
 
1.6%
61616
 
1.6%
71481
 
1.4%
81394
 
1.4%
91264
 
1.2%
Other values (310)29148
28.3%
ValueCountFrequency (%)
058135
56.5%
0.7224441073116
 
0.1%
12211
 
2.2%
1.7026647429
 
< 0.1%
22061
 
2.0%
2.6828853778
 
< 0.1%
31952
 
1.9%
3.6631060113
 
< 0.1%
41906
 
1.9%
4.6433266469
 
< 0.1%
ValueCountFrequency (%)
2421
 
< 0.1%
2371
 
< 0.1%
2292
 
< 0.1%
2271
 
< 0.1%
2261
 
< 0.1%
2251
 
< 0.1%
2243
< 0.1%
2223
< 0.1%
2213
< 0.1%
2196
< 0.1%

satisfaction
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
neutral or dissatisfied
58226 
satisfied
44599 

Length

Max length23
Median length23
Mean length16.92768296
Min length9

Characters and Unicode

Total characters1740589
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowneutral or dissatisfied
2nd rowneutral or dissatisfied
3rd rowsatisfied
4th rowneutral or dissatisfied
5th rowsatisfied

Common Values

ValueCountFrequency (%)
neutral or dissatisfied58226
56.6%
satisfied44599
43.4%

Length

2023-02-18T12:43:39.390004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:43:39.523038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
neutral58226
26.6%
or58226
26.6%
dissatisfied58226
26.6%
satisfied44599
20.3%

Most occurring characters

ValueCountFrequency (%)
i263876
15.2%
s263876
15.2%
e161051
9.3%
t161051
9.3%
a161051
9.3%
d161051
9.3%
r116452
6.7%
116452
6.7%
f102825
 
5.9%
n58226
 
3.3%
Other values (3)174678
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1624137
93.3%
Space Separator116452
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i263876
16.2%
s263876
16.2%
e161051
9.9%
t161051
9.9%
a161051
9.9%
d161051
9.9%
r116452
7.2%
f102825
 
6.3%
n58226
 
3.6%
u58226
 
3.6%
Other values (2)116452
7.2%
Space Separator
ValueCountFrequency (%)
116452
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1624137
93.3%
Common116452
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i263876
16.2%
s263876
16.2%
e161051
9.9%
t161051
9.9%
a161051
9.9%
d161051
9.9%
r116452
7.2%
f102825
 
6.3%
n58226
 
3.6%
u58226
 
3.6%
Other values (2)116452
7.2%
Common
ValueCountFrequency (%)
116452
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1740589
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i263876
15.2%
s263876
15.2%
e161051
9.3%
t161051
9.3%
a161051
9.3%
d161051
9.3%
r116452
6.7%
116452
6.7%
f102825
 
5.9%
n58226
 
3.3%
Other values (3)174678
10.0%

Interactions

2023-02-18T12:43:19.992447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:10.972407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:17.941849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:22.307638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:27.850610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:31.254186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:34.615642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:38.454242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:41.528682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:44.725622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:50.570804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:54.045364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:57.335335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:01.861232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:05.166751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:08.254526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:11.575041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:15.912991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:20.269845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:11.179625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:18.129336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:23.139342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:28.013524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:31.425560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:35.001667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:38.624425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:41.701030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:44.883574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:50.739047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:54.226803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:57.509176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:02.027049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:05.391432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:08.416755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:11.745865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:16.073358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:20.684346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:11.344022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:18.445168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:23.381691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:28.187664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:31.635567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:35.171957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:38.787343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:41.862175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:45.044174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:50.940072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:54.458009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:58.034918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:02.188086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:05.580249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:08.612223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:11.904451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:16.246461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:21.051642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:11.514327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:18.612228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:23.664012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:28.378456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:31.833137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:35.349032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:38.961082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:42.025164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:45.216479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:51.136896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:54.733077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:58.262253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:02.362823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:05.758941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:08.915754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:12.068195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:16.413388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:21.446235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:11.691823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:18.800766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:23.905058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:28.587174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:32.006788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:35.552924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:39.164843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:42.201257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:45.386665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:51.351387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:54.901058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:58.770672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:02.549940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:05.932998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:09.088868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:13.007499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:16.581822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:21.727575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:11.855659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:18.977600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:24.230136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:28.800381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:32.172829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:35.771979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:39.325141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:42.359947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:45.544956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:51.516789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:55.103305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:58.979253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:02.752685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:06.092218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:09.256271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:13.181741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:16.748442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:21.926585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:12.027111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:19.140564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:24.458082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:28.999331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:32.350892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:36.016591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:39.489572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:42.525576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:45.712907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:42:51.680537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-02-18T12:43:04.960438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:08.079954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:11.410950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:15.753787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:43:19.673404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Missing values

2023-02-18T12:43:25.195390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-18T12:43:26.774979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexUnnamed: 0idGenderCustomer TypeAgeType of TravelClassFlight DistanceInflight wifi serviceDeparture/Arrival time convenientEase of Online bookingGate locationFood and drinkOnline boardingSeat comfortInflight entertainmentOn-board serviceLeg room serviceBaggage handlingCheckin serviceInflight serviceCleanlinessDeparture Delay in MinutesArrival Delay in Minutessatisfaction
00070172MaleLoyal Customer13Personal TravelEco Plus4603431535543445525.018.0neutral or dissatisfied
1115047Maledisloyal Customer25Business travelBusiness235323313111531411.06.0neutral or dissatisfied
222110028FemaleLoyal Customer26Business travelBusiness1142222255554344450.00.0satisfied
33324026FemaleLoyal Customer25Business travelBusiness5622555222225314211.09.0neutral or dissatisfied
444119299MaleLoyal Customer61Business travelBusiness214333345533443330.00.0satisfied
555111157FemaleLoyal Customer26Personal TravelEco1180342112113444410.00.0neutral or dissatisfied
66682113MaleLoyal Customer47Personal TravelEco1276242322223343529.023.0neutral or dissatisfied
77796462FemaleLoyal Customer52Business travelBusiness2035434455555554544.00.0satisfied
88879485FemaleLoyal Customer41Business travelBusiness853122243311214120.00.0neutral or dissatisfied
99965725Maledisloyal Customer20Business travelEco1061333423322344320.00.0neutral or dissatisfied

Last rows

df_indexUnnamed: 0idGenderCustomer TypeAgeType of TravelClassFlight DistanceInflight wifi serviceDeparture/Arrival time convenientEase of Online bookingGate locationFood and drinkOnline boardingSeat comfortInflight entertainmentOn-board serviceLeg room serviceBaggage handlingCheckin serviceInflight serviceCleanlinessDeparture Delay in MinutesArrival Delay in Minutessatisfaction
10281510389410389486549MaleLoyal Customer26Business travelBusiness7124444555534434517.026.0satisfied
10281610389510389566030Femaledisloyal Customer24Business travelEco10551112111133554113.010.0neutral or dissatisfied
10281710389610389671445MaleLoyal Customer57Business travelEco867455544443431340.00.0neutral or dissatisfied
102818103897103897102203FemaleLoyal Customer60Business travelBusiness1599555555444444449.07.0satisfied
10281910389810389860666MaleLoyal Customer50Personal TravelEco1620313423224342420.00.0neutral or dissatisfied
10282010389910389994171Femaledisloyal Customer23Business travelEco192212322223142323.00.0neutral or dissatisfied
10282110390010390073097MaleLoyal Customer49Business travelBusiness2347444424555555540.00.0satisfied
10282210390110390168825Maledisloyal Customer30Business travelBusiness1995111341543245547.014.0neutral or dissatisfied
10282310390210390254173Femaledisloyal Customer22Business travelEco1000111511114515410.00.0neutral or dissatisfied
10282410390310390362567MaleLoyal Customer27Business travelBusiness1723133311111144310.00.0neutral or dissatisfied